A Music Classification Method Based on Timbral Features
We present a method for music classification based solely on the audio contents of the music signal. More specifically, the audio signal is converted into a compact symbolic representation that retains timbral characteristics and accounts for the temporal structure of a music piece. Models that capture the temporal dependencies observed in the symbolic sequences of a set of music pieces are built using a statistical language modeling approach. The proposed method is evaluated on two classification tasks (Music Genre classification and Artist Identification) using publicly available datasets. Finally, a distance measure between music pieces is derived from the method and examples of playlists generated using this distance are given. The proposed method is compared with two alternative approaches which include the use of Hidden Markov Models and a classification scheme that ignores the temporal structure of the sequences of symbols. In both cases the proposed approach outperforms the alternatives.
Date: 2010-Feb-19 Time: 15:00:00 Room: 336
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INESC-ID ESR Talks – February 2023
If you are a masters/PhD student or a postdoctoral fellow, come and present your work in an informal and friendly environment – and savour some tasty snacks!
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